input_layer: Construct an Input Layer

In tfestimators: Interface to 'TensorFlow' Estimators

Description

Returns a dense tensor as input layer based on given feature_columns. At the first layer of the model, this column oriented data should be converted to a single tensor.

Usage

input_layer(
  features,
  feature_columns,
  weight_collections = NULL,
  trainable = TRUE
)

Arguments

features	A mapping from key to tensors. Feature columns look up via these keys. For example column_numeric('price') will look at 'price' key in this dict. Values can be a sparse tensor or tensor depends on corresponding feature column.
feature_columns	An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from a dense column such as column_numeric(), column_embedding(), column_bucketized(), column_indicator(). If you have categorical features, you can wrap them with an column_embedding() or column_indicator().
weight_collections	A list of collection names to which the Variable will be added. Note that, variables will also be added to collections graph_keys()$GLOBAL_VARIABLES and graph_keys()$MODEL_VARIABLES.
trainable	If TRUE also add the variable to the graph collection graph_keys()$TRAINABLE_VARIABLES (see tf$Variable).

Value

A tensor which represents input layer of a model. Its shape is (batch_size, first_layer_dimension) and its dtype is float32. first_layer_dimension is determined based on given feature_columns.

Raises

• ValueError: if an item in feature_columns is not a dense column.

See Also

Other feature column constructors: column_bucketized(), column_categorical_weighted(), column_categorical_with_hash_bucket(), column_categorical_with_identity(), column_categorical_with_vocabulary_file(), column_categorical_with_vocabulary_list(), column_crossed(), column_embedding(), column_numeric()

tfestimators documentation built on Aug. 10, 2021, 1:06 a.m.